Exploratory analysis of forest harvest and regeneration pattern among multiple landowners
Bibliographic record
Abstract
A land cover map (1993) was combined with an updated forest change detection map (19912000) to examine forest harvest activity, mostly on private commercial forest lands. Landsat change detection methods indicated that industrial forest owners harvested a higher percentage of forest than non-industrial owners in a northern Maine study area. In the 1980s, the percentage of forest harvested across all ownership classes (five) was higher, the mean harvest patch size was larger, patches were more compact, and the mean perimeter to area ratios were smaller compared to data from the 1990s. For all patch metrics, there was a significant time period effect but there was no effect among landowners. Larger harvest patch size in the 1980s may be partially explained by extensive salvage logging that occurred in the wake of a massive spruce budworm infestation in the 1970s. Softwood types were dominant (> 80%) in regeneration stands approximately 1525 years old on all ownerships. Medium spatial resolution Landsat imagery shows promise as a landscape level tool to monitor forest change patterns and trends across multiple ownerships. Key words: remote sensing, Landsat, change detection, harvest intensity, forest regeneration, forest landowners
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".